To address these problems and to reduce reliance on purely supervised data, several speech enhancement and audio source separation studies have shifted their focus towards self-supervised methods [1]}.
In [2]}, a model was trained to estimate the signal-to-noise ratio (SNR) of noisy mixture recordings and assign a confidence value on each noisy segment. Next, a separation model was trained on the noisy speech mixtures using a weighted reconstruction loss function to filter out the contribution of noisy ground-truth speech utterances.
Lately, mixture invariant training (MixIT) has been proposed in [3]} which enables unsupervised training of separation models by generating artificial mixtures of mixtures (MoMs) and letting the separation model estimate and reassign the sources back to the ground-truth mixtures. MixIT has been experimentally shown to provide a robust unsupervised solution for speech enhancement under a variety of setups [3]}, [5]}, [6]}. However, MixIT assumes access to in-domain noise samples and slightly alters the input SNR distribution by training on artificial MoMs with more than one noise sources [7]}.
